A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study

• Published in: BMC bioinformatics Vol. 15; no. Suppl 17; p. S3
• Main Authors: Chen, Zhongxue, Yang, William, Liu, Qingzhong, Yang, Jack Y, Li, Jing, Yang, Mary Qu
• Format: Journal Article
• Language: English
• Published: London BioMed Central 16.12.2014; Springer Nature B.V
• Subjects: Algorithms; Bioinformatics; Biology; Biomedical and Life Sciences; Case-Control Studies; Computation; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer science; Computer Simulation; Conferences; Confidence intervals; Data processing; Genome-Wide Association Study; Genomes; Humans; Hypotheses; Independent study; Information technology; Inverse; Life Sciences; Microarrays; Models, Statistical; Probability distribution functions; Public health; Random variables; Statistical methods; Studies; Indiana; United States > US; Arkansas; Massachusetts; Fisher test; z-test; Lancaster method; rare variant association test
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-15-S17-S3

Background Combining information from different studies is an important and useful practice in bioinformatics, including genome-wide association study, rare variant data analysis and other set-based analyses. Many statistical methods have been proposed to combine p-values from independent studies. However, it is known that there is no uniformly most powerful test under all conditions; therefore, finding a powerful test in specific situation is important and desirable. Results In this paper, we propose a new statistical approach to combining p-values based on gamma distribution, which uses the inverse of the p-value as the shape parameter in the gamma distribution. Conclusions Simulation study and real data application demonstrate that the proposed method has good performance under some situations.